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robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Tianci Liu Hengjia Qin Zhuo Zhan Yunpeng Liu Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Laboratory of Image Understanding and Computer Vision Shenyang Liaoning Province China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Shenyang Liaoning Province China

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database.

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Multiscale Convolutional Transformer with Diverse-aware Feature Learning for Motor Imagery EEG Decoding

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong

Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EEG features across different frequency ranges remains a significant challenge, as the homogenization of feature representations often limits the generalization capabilities of EEG decoding models for BCIs. In this paper, we propose a novel multiscale convolutional transformer framework for EEG decoding that integrates multiscale convolution, transformer, and diverse-aware feature learning scheme (MCTD) to tackle the above challenge. Specifically, we first capture multiple frequency features using dynamic one-dimensional temporal convolution with different kernel lengths. Subsequently, we incorporate convolutional layers and transformers with a contrastive learning scheme to extract discriminative local and global EEG features within a single frequency range. To mitigate the homogenization of features extracted from different frequency ranges, we propose a novel decorrelation regularization. It enables multiscale convolutional transformers produce less correlated features with each other, thereby enhancing the overall expressiveness of EEG decoding model. The performance of MCTD is evaluated on four public MI-based EEG datasets, including the BCI competition III 3a and IV 2a, the BNCI 2015- 001, and the OpenBMI. For the average Kappa/Accuracy scores, MCTD obtains improvements of 3.58%/2.68%, 3.09%/2.20%, 2.33%/1.54%, and 4.44%/2.22%, over the state-of-the-art method on four EEG datasets, respectively. Experimental results demonstrate that our method exhibits superior performance. © 2016 IEEE.

关键词： Brain computer interface

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Development of a Twisted String Actuator-based Exoskeleton for Hip Joint Assistance in Lifting Tasks

Development of a Twisted String Actuator-based Exoskeleton f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hyeon-Seok Seong Do-Hyeong Kim Igor Gaponov Jee-Hwan Ryu Department of Civil and Environmental Engineering KAIST Daejeon South Korea Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

This paper presents a study on a compliant cable-driven exoskeleton for hip assistance in lifting tasks that is aimed at preventing low-back pain and injuries in the vocational setting. In the proposed concept, we used twisted string actuator (TSA) to design a light-weight and powerful exoskeleton that benefits from inherent TSA advantages. We have noted that nonlinear nature of twisted strings' transmission ratio (decreasing with twisting) closely matched typical torque-speed requirements for hip assistance during lifting tasks and tried to use this fact in the exoskeleton design and motor selection. Hip-joint torque and speed required to lift a 10-kg load from stoop to stand were calculated, which gave us a baseline that we used to design and manufacture a practical exoskeleton prototype. Preliminary experimental trials demonstrated that the proposed device was capable of generating required torque and speed at the hip joint while weighing under 6 kg, including battery.

关键词： Hip Exoskeletons Torque Pulleys Actuators Kinematics Task analysis

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Errata: Mathematical Modeling and Designing a Heavy Hybrid-Electric Quadcopter, Controlled by Flaps

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Unmanned Systems 2024年第1期12卷 183-183页

作者： Mohammad Sadeq Ale Isaac Ahmed Refaat Ragab Enrique Caballero Garcés Marco Andres Luna Pablo Flores Peña Pascual Campoy Cervera Drone Hopper Company Spain Computer Vision and Aerial Robotics Group at Universidad Politécnica de Madrid (CVAR-UPM) at Centre for Automation and Robotics C.A.R. (UPM-CSIC) Spain University of Carlos III of Madrid Spain UC3M and Aerodynamics Engineer at Drone Hopper Spain

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vision-Based Goal-Conditioned policies for underwater navigation in the presence of obstacles

arXiv

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arXiv 2020年

作者： Manderson, Travis Gamboa, Juan Camilo Wapnick, Stefan Tremblay, Jean-François Shkurti, Florian Meger, Dave Dudek, Gregory Mobile Robotics Laboratory School of Computer Science McGill University Montreal Canada Robot Vision & Learning Lab Department of Computer Science University of Toronto Canada

We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot’s relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions. Copyright © 2020, The Authors. All rights reserved.

关键词： Navigation

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Accurate Mapping and Planning for Autonomous Racing

Accurate Mapping and Planning for Autonomous Racing

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Leiv Andresen Adrian Brandemuehl Alex Honger Benson Kuan Niclas Vödisch Hermann Blum Victor Reijgwart Lukas Bernreiter Lukas Schaupp Jen Jen Chung Mathias Burki Martin R. Oswald Roland Siegwart Abel Gawel Autonomous Systems Lab ETH Zürich Sevensense Robotics AG Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

This paper presents the perception, mapping, and planning pipeline implemented on an autonomous race car. It was developed by the 2019 AMZ driverless team for the Formula Student Germany (FSG) 2019 driverless competition, where it won 1st place overall. The presented solution combines early fusion of camera and LiDAR data, a layered mapping approach, and a planning approach that uses Bayesian filtering to achieve high-speed driving on unknown race tracks while creating accurate maps. We benchmark the method against our team's previous solution, which won FSG 2018, and show improved accuracy when driving at the same speeds. Furthermore, the new pipeline makes it possible to reliably raise the maximum driving speed in unknown environments from 3 m/s to 12 m/s while still mapping with an acceptable RMSE of 0.29 m.

关键词： Three-dimensional displays Filtering Pipelines Robustness Planning Bayes methods Automobiles

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PC-HMR: Pose calibration for 3d human mesh recovery from 2D images/videos

arXiv

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arXiv 2021年

作者： Luan, Tianyu Wang, Yali Zhang, Junhao Wang, Zhe Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China University of California Irvine United States

The end-to-end Human Mesh Recovery (HMR) approach (Kanazawa et al. 2018) has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh often exhibits incorrect pose for complex activities. To tackle this problem, we propose to exploit 3D pose to calibrate human mesh. Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in a serial or parallel manner, these two frameworks can effectively correct human mesh with guidance of a concise pose calibration module. Furthermore, since the calibration module is designed via non-rigid pose transformation, our PCHMR frameworks can flexibly tackle bone length variations to alleviate misplacement in the calibrated mesh. Finally, our frameworks are based on generic and complementary integration of data-driven learning and geometrical modeling. Via plug-and-play modules, they can be efficiently adapted for both image/video-based human mesh recovery. Additionally, they have no requirement of extra 3D pose annotations in the testing phase, which releases inference difficulties in practice. We perform extensive experiments on the popular benchmarks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks achieve the SOTA results. Copyright © 2021, The Authors. All rights reserved.

关键词： Mesh generation

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Navigation Command Matching for vision-based Autonomous Driving

Navigation Command Matching for Vision-based Autonomous Driv...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yuxin Pan Jianru Xue Pengfei Zhang Wanli Ouyang Jianwu Fang Xingyu Chen Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China SenseTime Computer Vision Research Group University of Sydney Australia

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Learning an optimal policy for autonomous driving task to confront with complex environment is a long- studied challenge. Imitative reinforcement learning is accepted as a promising approach to learn a robust driving policy through expert demonstrations and interactions with environments. However, this model utilizes non-smooth rewards, which have a negative impact on matching between navigation commands and trajectory (state-action pairs), and degrade the generalizability of an agent. Smooth rewards are crucial to discriminate actions generated from sub-optimal policy. In this paper, we propose a navigation command matching (NCM) model to address this issue. There are two key components in NCM, 1) a matching measurer produces smooth navigation rewards that measure matching between navigation commands and trajectory; 2) attention-guided agent performs actions given states where salient regions in RGB images (i.e. roadsides, lane markings and dynamic obstacles) are highlighted to amplify their influence on the final model. We obtain navigation rewards and store transitions to replay buffer after an episode, so NCM is able to discriminate actions generated from suboptimal policy. Experiments on CARLA driving benchmark show our proposed NCM outperforms previous state-of-the- art models on various tasks in terms of the percentage of successfully completed episodes. Moreover, our model improves generalizability of the agent and obtains good performance even in unseen scenarios.

关键词： Navigation Task analysis Trajectory Learning (artificial intelligence) Autonomous vehicles Smoothing methods Current measurement

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Wavelet Integrated CNNs for Noise-Robust Image Classification

arXiv

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arXiv 2020年

作者： Li, Qiufu Shen, Linlin Guo, Sheng Lai, Zhihui Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence & Robotics for Society China Malong Technologies

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions. Copyright © 2020, The Authors. All rights reserved.

关键词： Image classification

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Abstract: Learning to avoid poor images: towards task-aware c-arm cone-beam ct trajectories

Abstract: Learning to avoid poor images: towards task-aware ...

引用

International workshop on Algorithmen - Systeme - Anwendungen, 2020

作者： Zaech, Jan-Nico Gao, Cong Bier, Bastian Taylor, Russell Maier, Andreas Navab, Nassir Unberath, Mathias Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Computer Vision Laboratory Eidgenössische Technische Hochschule Zürich Zürich Germany

ISBN: (纸本)9783658292669

Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patient-specific manner, and describe the first step towards the envisioned task-aware CBCT protocol. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020.

关键词： computerized tomography

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